| Autor: |
Bayer, Florian P., Gander, Manuel, Kuster, Bernhard, The, Matthew |
| Předmět: |
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| Zdroj: |
Nature Communications; 7/19/2024, Vol. 14 Issue 1, p1-11, 11p |
| Abstrakt: |
Dose-response curves are key metrics in pharmacology and biology to assess phenotypic or molecular actions of bioactive compounds in a quantitative fashion. Yet, it is often unclear whether or not a measured response significantly differs from a curve without regulation, particularly in high-throughput applications or unstable assays. Treating potency and effect size estimates from random and true curves with the same level of confidence can lead to incorrect hypotheses and issues in training machine learning models. Here, we present CurveCurator, an open-source software that provides reliable dose-response characteristics by computing p-values and false discovery rates based on a recalibrated F-statistic and a target-decoy procedure that considers dataset-specific effect size distributions. The application of CurveCurator to three large-scale datasets enables a systematic drug mode of action analysis and demonstrates its scalable utility across several application areas, facilitated by a performant, interactive dashboard for fast data exploration. Dose-response curves are ubiquitous in pharmacology and biology, yet potency and effect size are often estimated even when there is no response. Here, authors present a statistical framework to assess curve significance and demonstrate how this aids drug mode of action analysis in large public datasets. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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